def test_preemphasis(): for dtype in [np.float32, np.float64]: np.random.seed(1234) x = np.random.rand(16000 * 5).astype(dtype) y = preemphasis(x, 0.97) assert x.shape == y.shape assert x.dtype == y.dtype x_hat = inv_preemphasis(y, 0.97) assert x_hat.dtype == x.dtype assert np.allclose(x_hat, x, atol=1e-5) x = np.random.rand(16000) assert np.allclose(P.preemphasis(x, 0), x) assert np.allclose(P.inv_preemphasis(P.preemphasis(x, 0), 0), x)
def inv_spectrogram(spectrogram): '''Converts spectrogram to waveform using librosa''' S = _db_to_amp(_denormalize(spectrogram) + hparams.ref_level_db) # Convert back to linear processor = _lws_processor() D = processor.run_lws(S.astype(np.float64).T ** hparams.power) y = processor.istft(D).astype(np.float32) return inv_preemphasis(y)
def inv_preemphasis(x): # allow disable preemphasis if hparams.preemphasis == -1.0: return x else: from nnmnkwii.preprocessing import inv_preemphasis return inv_preemphasis(x, hparams.preemphasis)
def inv_mel_spectrogram(mel_spectrogram): D = _denormalize(mel_spectrogram) S = _mel_to_linear( _db_to_amp(D + hparams.ref_level_db)) # Convert back to linear processor = _lws_processor() D = processor.run_lws(S.astype(np.float64).T**hparams.power) y = processor.istft(D).astype(np.float32) return inv_preemphasis(y)
def inv_spectrogram(spectrogram): '''Converts spectrogram to waveform using librosa''' S = _db_to_amp(_denormalize(spectrogram) + hparams.spec_ref_level_db) # Convert back to linear #S = librosa.db_to_amplitude(_denormalize(spectrogram) + hparams.spec_ref_level_db) D = librosa.griffinlim(S**hparams.power, hop_length=hparams.hop_size, win_length=hparams.fft_wsize) return inv_preemphasis(D)
def inv_preemphasis(x): from nnmnkwii.preprocessing import inv_preemphasis return inv_preemphasis(x, hparams.preemphasis)
def inv_preemphasis(x, coef=0.85): return P.inv_preemphasis(x, coef)
def _inv_preemphasis(self, x): from nnmnkwii.preprocessing import inv_preemphasis return inv_preemphasis(x, self.hparams.preemphasis)
def inv_preemphasis(x): from nnmnkwii.preprocessing import inv_preemphasis return inv_preemphasis(x, preemphasis_factor)